Alejandro Cervantes

Universidad Internacional de La Rioja | UNIR · UNIR Research

Convective weather is a large source of disruption for air traffic management operations. Being able to predict thunderstorms the day before operations can help traffic managers plan around weather and improve air traffic flow management operations. In this paper, machine learning is applied on data from satellite storm observations and ensemble nu...

A main objective of Air Traffic Flow Management is matching airspace and airport capacity with demand. Being able to accurately predict unexpected disruptions to the air traffic network, such as convective weather is essential in order to make better informed decisions and improve performance of the system. In this paper we demonstrate how machine...

This study explores the suitability of neural networks with a convolutional component as an alternative to traditional multilayer perceptrons in the domain of trend classification of cryptocurrency exchange rates using technical analysis in high frequencies. The experimental work compares the performance of four different network architectures -con...

The home care and scheduling problem (HCSP) consists on the design of a set of routes to be used by caregivers that provide daily assistance at specific times to patients located in a definite geographic area. In this study we propose a modified version of Ant Colony Optimization (ACO), called IACS-HCSP, to approach this task. In order to be used i...

We have compared the performance of different machine learning techniques for human activity recognition. Experiments were made using a benchmark dataset where each subject wore a device in the pocket and another on the wrist. The dataset comprises thirteen activities, including physical activities, common postures, working activities and leisure a...

In recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the non-stationary nature and the likelihood of drastic structural or concept changes in the markets. Traditional systems are unable or slow to adapt to these changes. Ensemble-based systems are wid...

Incremental learning from non-stationary data poses special challenges to the field of machine learning. Although new algorithms have been developed for this, assessment of results and comparison of behaviors are still open problems, mainly because evaluation metrics, adapted from more traditional tasks, can be ineffective in this context. Overall,...

The home care crew scheduling (HCCS) problem is a planning task whose goal is to allocate a set of professional caregivers in the most efficient way to perform a number of assistencial and health care visits to the customers private homes. This is part of an important trend in advanced health care systems, to promote “independent living” specially...

This paper tries to tackle the modern challenge of practical steganalysis over large data by presenting a novel approach whose aim is to perform with perfect accuracy and in a completely automatic manner. The objective is to detect changes introduced by the steganographic process in those data objects, including signatures related to the tools bein...

Appendix I. Example of Classification using a Naive Bayes Model. (PDF)

Appendix II. Experimental Validation. (PDF)

This work explores the connection between psychological well-being and Internet use in older adults. The study is based on a sample of 2314 participants in the English Longitudinal Study of Aging. The subjects, aged 50 years and older, were interviewed every two years over the 2006–2007 to 2014–2015 period. The connection between the use of Interne...

The Home Health Care Scheduling Problem involves allocating professional caregivers to patients’ places of residence to meet service demands. These services are regular in nature and must be provided at specific times during the week. In this paper, we present a heuristic with two tie-breaking mechanisms suitable for large-scale versions of the pro...

In the context of forecasting for renewable energy, it is common to produce point forecasts but it is also important to have information about the uncertainty of the forecast. To this extent, instead of providing a single measure for the prediction, lower and upper bound for the expected value for the solar radiation are used (prediction interval)....

Stream Processing has recently become one of the current commercial trends to face huge amounts of data. However, normally these techniques need specific infrastructures and high resources in terms of memory and computing nodes. This paper shows how mini-batch techniques and topology extraction methods can help making gigabytes of data to be manage...

Multi-objective optimization problems are often subject to the presence of objectives that require expensive resampling for their computation. This is the case for many robustness metrics, which are frequently used as an additional objective that accounts for the reliability of specific sections of the solution space. Typical robustness measurement...

As the population ages, the demand for home health care services is increasing currently, and is expecting to keep growing in the future. To cope with this increasing demand, effective planning approaches for assigning caregivers to vistis at patients' home are required. The aim of this paper is to address this planning problem with parallel a (1 +...

Learning from non-stationary data requires methods that are able to deal with a continuous stream of data instances, possibly of infinite size, where the class distributions are potentially drifting over time. For handling such datasets, we are proposing a new method that incrementally creates and adapts a network of prototypes for classifying comp...

The Software Project Scheduling (SPS) problem refers to the distribution of tasks during a software project lifetime. Software development involves managing human resources and a total budget in an optimal way for a successful project which, in turn, demonstrates the importance of the SPS problem for software companies. This paper proposes a novel...

Nearest prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper, we first use the standard particle swarm optimize...

The aim os this paper is to study the hybridization of two multi-objective algorithms in the context of a real problem, the MANETs problem. The algorithms studied are Particle Swarm Optimization (MOPSO) and a new multiobjective algorithm based in the combination of NSGA-II with Evolution Strategies (ESN). This work analyzes the improvement produced...

This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its applica- tion to continuous classi cation problems as a Nearest Prototype (NP) classi er. In Nearest Prototype classi ers, a collection of prototypes has to be found that accurately represents the input patterns. The classi er then assign...

This work presents the application of a parallel cooperative optimization approach to the broadcast operation in mobile ad-hoc networks (manets). The optimization of the broadcast operation implies satisfying several objectives simultaneously, so a multi-objective approach has been designed. The optimization lies on searching the best configuration...

Los Clasificadores por Vecino ms Prximo (K-NN) han recibido un impulso renovado con la aplicación de metaheursticas de búsqueda (evolutivas, etc.) que permiten optimizar su funcionamiento, mediante la optimización de atributos, reducción de prototipos, y evolución de medidas globales o locales de proximidad; ello permite desarrollar clasificadores...

11th International Conference on Computer Aided Systems Theory. Las Palmas de Gran Canaria, Spain, February 12-16, 2007 Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such scenario, broadcasting becomes an operation of tremendous...

A mobile adhoc network (MANETs) is a self-configuring network of mobile routers (and associated hosts). The routers tend to move randomly and organize themselves arbitrarily; thus, the network's wireless topology may change fast and unpredictably. Nowadays, these networks are having a great influence due to the fact that they can create networks wi...

Nearest Prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of proto- types has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collec- tion. In this paper we develop a new algorithm (called AMPSO), bas...

This paper presents an application of particle swarm optimization (PSO) to continuous classification problems, using a Michigan approach. In this work, PSO is used to process training data to find a reduced set of prototypes to be used to classify the patterns, maintaining or increasing the accuracy of the nearest neighbor classifiers. The Michigan...

Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such scenario, broadcasting becomes an operation of tremendous importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multio...

This paper shows the performance of the binary PSO algorithm as a classification system. These systems are classified in two different perspectives: the Pittsburgh and the Michigan approaches. In order to implement the Michigan approach binary PSO algorithm, the standard PSO dynamic equations are modified, introducing a repulsive force to favor par...

Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the results of same well known Machine Learning methods in the resolution of discrete classification problems. A binary version of the PSO algorithm is used to obtain a set of logic rules that map binary masks (that represent the attribute values), lo the ava...

Thispapershowstheperformance oftheBi- naryPSOAlgorithm asaclassification system. These systems areclassified intwodifferent perspectives: the Pittsburgh andtheMichigan approaches. Inorderto implement theMichigan Approach Binary PSOAlgo- rithm, thestandard PSOdynamic equations aremodi- fied, introducing arepulsive force tofavor particle com- petitio...